Traditionally, physicians have been expected to retain in memory knowledge relating to potential adverse drug reactions, pharmacology, and pharmacogenetics, or to have access to such information from published (generally hard-copy) reports—information that is not accessible from a single source and which is increasingly complex. More recently, some classes of information, for example labeling warnings published in the PDR, have become accessible through wireless and PDA devices. There is increasing interest in expanding the availability of this kind of information at the point of care.
A basic problem with all such information, however, is the need for computer systems, databases, networks, and software tools to display and bring to the foreground the information most relevant to the issue at hand, a problem that requires extensive software development. In short, it is no longer sufficient to merely publish information in the form of a text book or a reference manual. But because of the difficulty in obtaining reimbursement for the costs of the software services and databases, progress toward sustainable software innovation and deployment has been disappointing. Thus, there is a need in the medical arts for business models to support computerized implementation of systems designed to store and process metabolic, pharmacologic, and pharmacogenetic data (herein “metabolomic data”), to interpret that data in the context of patient-specific factors such as age, pregnancy, smoking and use of alcohol (herein “clinical factors”, or “patient characteristics”), to make available that data at the point of care, prioritized by relevance, and to provide integrated reimbursement tools for the costs of the equipment, database updates and maintenance. Needed are business models that support implementations of these computerized tools.
Mental Health Connections (Lexington, Mass.) was an early entrant into computerized medical bioinformatics services. Their GeneMedRx service, introduced in 1995, was initially based on computerized tables for looking up drug interactions as a function of induction or inhibition of the cytochrome P450s involved in their metabolism. In 2006, in partnership with Genelex (Seattle Wash.) testing was begun on systems for interpretation of drug-drug and drug-gene interactions within the framework of a patient's overall medication regimen. In recent versions, GeneMedRx has grown as a database and now recognizes transporter and conjugation-linked as well as cytochrome P450-linked drug interactions. The drug interaction service now also includes a novel predictive algorithm. These efforts have provided valuable lessons in the need for improved business models to successfully commercialize various aspects of bioinformatics.
Marchand (USPA 2006/0289019) describes computer systems for optimizing medical treatment based on pharmacogenetic testing and Pareto modelling. But the disclosure is silent as to how to pay for these systems. Pickar (USPA 2003/0104453) describes computer systems for minimizing adverse drug events but is silent with respect to means to recover the costs. Hoffman (USPA 2004/0197813 and USPA 2004/0199333) describes a method for determining whether an atypical response to drug therapy is attributable to an error in metabolism but again does not describe a business method. Early work describing the application of computers to pharmacogenetics is described in a 1999 paper by Evans and Relling (Science 286:487-491), a 2000 paper by Ichikawa (Internal Medicine 39:523-24), and in a patent application that same year by Reinhoff (US 2002/0049772). Reinhoff describes a computer implementation of a program on a networked computer for analyzing polymorphisms in human populations and using this information to, for example, “gauge drug responses”, but these citations again do not address reimbursement concerns.
Although Gill-Garrison in U.S. Pat. No. 7,054,758 describes computer preparation and delivery of genetic reports that include “personalized dietary advice”, the report service as commercialized (Sciona, Boulder Colo.) is limited to direct marketing to consumers by the testing laboratory under ‘fee-for-service’ arrangements and does not contemplate methods for billing such as ‘pass-through’ reimbursement models or wholesale services to contract laboratories or clinics. Nor does the service quantitate or extrapolate the effects of impacting substances or factors (as practiced and defined here) on the pharmacokinetics of drug metabolism, for preparation of reports relying instead on a simple look-up table or tables to correlate “advice” with “risk factor” and genetic polymorphism.
Holden (USPA 2004/0088191) addresses the issue of secure access to genetic test results over a network and the use of passwords to share genetic test data with third parties such as physicians. Dodds (USPA 2003/0135096) again recognizes the security issues, but sees that secure access can be linked to payment authorization in a simple fee-for-service model with on-line authorization of credit card purchases. Issued U.S. Pat. No. 7,054,755 also proposes prior art financial service means, specifically means to purchase genetic testing kits electronically, in what is basically a shopping cart model such as might have been assembled from the teachings of U.S. Pat. No. 5,960,411, the “one-click” patent to Amazon.com, and related arts.
However, an invitation to the customer to pay directly for preventative medical care, for example genetic testing, has not been generally appealing or successful. More typically, customers will habitually defer the costs of preventative medicine. Thus, whereas Larder in U.S. Pat. No. 7,058,616 states, “The main challenge in genotyping is the interpretation of the results” (Col 9, lines 27-28), to the contrary we have found that the main challenge is supporting the costs of the required servers, databases, networks and programming. A particularly preferred model, as disclosed here, eliminates the need for mental processes in operation of the system. Genetic testing services are thus still in need of improved business models built on automated systems, business models capable of generating sufficient revenue to support their development and implementation at the point of care.